# Copyright 2019-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
from __future__ import absolute_import

import os

import pytest

from sagemaker import utils
from sagemaker.mxnet.model import MXNetModel

from ..... import invoke_sm_helper_function
from ...integration import RESOURCE_PATH
from ...integration.sagemaker import timeout

DEFAULT_HANDLER_PATH = os.path.join(RESOURCE_PATH, "default_handlers")
MODEL_PATH = os.path.join(DEFAULT_HANDLER_PATH, "model.tar.gz")
SCRIPT_PATH = os.path.join(DEFAULT_HANDLER_PATH, "model", "code", "empty_module.py")


@pytest.mark.integration("hosting")
@pytest.mark.team("frameworks")
@pytest.mark.model("linear_regression")
def test_hosting(
    ecr_image, sagemaker_regions, instance_type, framework_version, skip_neuron_containers
):
    invoke_sm_helper_function(
        ecr_image, sagemaker_regions, _test_hosting_function, instance_type, framework_version
    )


def _test_hosting_function(ecr_image, sagemaker_session, instance_type, framework_version):
    prefix = "mxnet-serving/default-handlers"
    model_data = sagemaker_session.upload_data(path=MODEL_PATH, key_prefix=prefix)
    model = MXNetModel(
        model_data,
        "SageMakerRole",
        SCRIPT_PATH,
        framework_version=framework_version,
        image_uri=ecr_image,
        sagemaker_session=sagemaker_session,
    )

    endpoint_name = utils.unique_name_from_base("test-mxnet-serving")
    with timeout.timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
        predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)

        output = predictor.predict([[1, 2]])
        assert [[4.9999918937683105]] == output
